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      <title>IK中文分词器如何分词 - 交互式测验</title>
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      </style>
    </head>
    <body>
      <div class="main-wrapper">
          <div class="quiz-container">
            <div class="quiz-header">
              <h1>IK中文分词器如何分词 - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
            </div>
            <div id="quiz-content">
                <div class="quiz-body">
                    <div class="progress-bar">
                        <div id="progress-indicator"></div>
                    </div>
                    <div id="question-container">
                      <p id="question-text"></p>
                      <ul class="options-list" id="options-list"></ul>
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                    <div id="result-container"></div>
                    <div id="hint-container" class="hint-container"></div>
                </div>
                <div class="quiz-footer" id="quiz-footer">
                  <button id="prev-btn" class="nav-btn">上一题</button>
                  <button id="hint-btn" class="nav-btn" style="background-color: #ffc107; color: #333; box-shadow: 0 4px 12px rgba(255,193,7,0.3);">提示</button>
                  <button id="next-btn" class="nav-btn">下一题</button>
                </div>
            </div>
          </div>
      </div>

      <script>
        const quizData = [
  {
    "question": "根据文档，IK分词器提供了两种主要的分词模式。在需要进行全文检索、对精度要求较高的场景中，应该选择哪种模式？",
    "options": [
      "智能模式 (Smart Mode)",
      "最大化匹配模式",
      "细粒度模式 (Fine-Grained Mode)",
      "词典匹配模式"
    ],
    "correctAnswerIndex": 2,
    "explanation": "细粒度模式会尽可能地将文本分割成更多的词语，这种方式可以提高检索的召回率和准确性，因此特别适合全文检索等对精度要求高的应用场景。",
    "source": "文档中「1. 分词模式」部分",
    "hint": "文档中提到，有一种模式“会尝试将句子分割成更多的词语，以提高检索的准确性”。"
  },
  {
    "question": "IK分词器的“智能模式” (Smart Mode) 相较于“细粒度模式”最核心的特点是什么？",
    "options": [
      "它会根据常用词汇和语法规则进行分词，并尝试消除歧义，以产生更自然的词组。",
      "它会将文本分割成尽可能短的词语，以实现最高效的索引。",
      "它主要依赖用户自定义词典，不对内置词典进行匹配。",
      "它会跳过所有停用词（stop words）来提升处理速度。"
    ],
    "correctAnswerIndex": 0,
    "explanation": "智能模式的目标是模拟人类的语言习惯，将文本分割成较为自然的词组。为此，它不仅进行词典匹配，还会执行“消除歧义”的步骤，从多种可能的分词结果中选择最合理的一种。",
    "source": "文档中「1. 分词模式」和「4. 分词过程」下的「3、消除歧义」部分",
    "hint": "这种模式的分词结果更适合“对用户体验要求高的场景”，并且它会进行一个额外的处理步骤来“寻找最合理的一种方式”。"
  },
  {
    "question": "如果用户需要在分词时识别一些特定领域的专有词汇（如医学术语、公司产品名），应该利用IK分词器的哪个特性？",
    "options": [
      "词性标注功能",
      "支持自定义词典 (ext.dic)",
      "高效的Trie树（前缀树）数据结构",
      "智能模式的歧义消除算法"
    ],
    "correctAnswerIndex": 1,
    "explanation": "IK分词器允许用户通过配置扩展词典（ext.dic）来添加自定义词汇。这样，分词器就能识别出主词典中未包含的特定术语，极大地提高了其在特定应用场景下的适应性。",
    "source": "文档中「3. IK 分词器的特点」和「4. 分词过程」下的「1、加载词典」部分",
    "hint": "文档中提到了一个名为 `ext.dic` 的词典文件，思考一下它的作用是什么？"
  },
  {
    "question": "在IK分词器的智能模式下，当出现多种可能的分词组合时，系统会根据一系列规则进行“消除歧义”。以下哪一项是优先级最高的判断规则？",
    "options": [
      "分词个数越少越好",
      "词长越平均越好",
      "分词文本长度越长越好",
      "分词位置越靠后的优先"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档中明确列出了IK歧义判断的六条规则，并指出其优先级是自上而下降低的。其中，第一条也是优先级最高的规则是“分词文本长度越长越好”，这意味着系统倾向于选择那些由更长的词语组成的分词方案。",
    "source": "文档中「4. 分词过程」下的「3、消除歧义」部分",
    "hint": "文档在“消除歧义”部分列出了一系列规则，并说明了它们的优先级顺序。请查看列表中的第一条规则。"
  },
  {
    "question": "文档中提到，IK分词器在加载词典时使用了一种高效的树形数据结构来存储和查询词汇。这种数据结构是什么？",
    "options": [
      "B+树",
      "Trie树 (前缀树)",
      "红黑树",
      "哈希表"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档中明确指出，IK分词器加载词典时使用了“常用的字典树 tire tree 是一种结构简单的树，也叫做前缀树”。这种结构非常适合进行快速的词汇前缀匹配，是分词器实现高效词典查找的关键。",
    "source": "文档中「4. 分词过程」下的「1、加载词典」部分",
    "hint": "这种数据结构也被称为“字典树”，它的一个关键特点是能高效地处理词汇的前缀。"
  },
  {
    "question": "根据文档描述，对于一个可以有多种切分方式的语句，非smart模式（细粒度模式）和smart模式（智能模式）在处理上最根本的区别是什么？",
    "options": [
      "非smart模式会输出所有可能的分词结果，而smart模式会进行一次选择，只输出它认为最合理的一种。",
      "非smart模式只使用主词典，而smart模式会使用所有词典。",
      "smart模式处理速度远快于非smart模式。",
      "非smart模式不支持自定义词典，只有smart模式支持。"
    ],
    "correctAnswerIndex": 0,
    "explanation": "文档中通过对比图和文字说明指出，“非smart模式可以认为是最小力度的分词，把分出来的词全部输出”，而“smart模式，就是需要在这几种分词模式中，寻找一种认为最合理的分词方式”，这个过程就是“消除歧义”。因此，根本区别在于输出结果是“全部可能”还是“最优选择”。",
    "source": "文档中「4. 分词过程」下的「2、词的匹配」部分",
    "hint": "文档在对比两种模式时提到，一种模式会“把每种可能的分词结果都给出来了”，而另一种则会进行“分词的选择”。"
  },
  {
    "question": "根据文档，IK分词器在处理一段文本时的主要逻辑步骤顺序是什么？",
    "options": [
      "词性标注 -> 加载词典 -> 词的匹配",
      "加载词典 -> 词的匹配 -> 消除歧义（在smart模式下）",
      "词的匹配 -> 消除歧义 -> 过滤停用词",
      "加载词典 -> 词性标注 -> 合并和过滤"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档在「4. 分词过程」的开头就概括了主要逻辑，包括三大部分：1）加载词典，2）词的匹配(分词)，3）消除歧义。词性标注是可选的，合并和过滤是分词流程中的一个环节，但核心逻辑是这三步。",
    "source": "文档中「4. 分词过程」部分",
    "hint": "文档的第四部分开头就总结了分词处理的“主要逻辑”，它包括三个部分。"
  }
];
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        let score = 0;
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            document.getElementById('quiz-footer').style.display = 'none';

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            const totalQuestions = quizData.length;
            const answeredCount = userAnswers.filter(a => a !== null).length;
            const wrongCount = answeredCount - correctCount;
            const skippedCount = totalQuestions - answeredCount;
            const accuracy = totalQuestions > 0 ? Math.round((correctCount / totalQuestions) * 100) : 0;
            const errorRate = totalQuestions > 0 ? Math.round((wrongCount / totalQuestions) * 100) : 0;

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                '<div class="final-score-container">' +
                '<h2>答题报告</h2>' +
                '<div class="score-summary">' +
                '<div class="score-card">' +
                '<h3>得分</h3>' +
                '<p>' + correctCount + ' / ' + totalQuestions + '</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>正确率</h3>' +
                '<p>' + accuracy + '%</p>' +
                '</div>' +
                '<div class="score-card">' +
                '<h3>错误率</h3>' +
                '<p>' + errorRate + '%</p>' +
                '</div>' +
                '<div class="score-card score-details">' +
                '<table>' +
                '<tr><td>正确</td><td>' + correctCount + '</td></tr>' +
                '<tr><td>错误</td><td>' + wrongCount + '</td></tr>' +
                '<tr><td>未答</td><td>' + skippedCount + '</td></tr>' +
                '</table>' +
                '</div>' +
                '</div>' +
                '<div class="report-actions">' +
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                '<button class="primary" onclick="location.reload()">再试一次</button>' +
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            }
            quizContentEl.insertAdjacentHTML('beforeend', finalScoreHtml);
            document.getElementById('review-btn').addEventListener('click', reviewQuiz);
        }

        function reviewQuiz() {
            const reportEl = document.querySelector('.final-score-container');
            if (reportEl) {
                reportEl.remove();
            }

            document.querySelector('.quiz-body').style.display = 'block';
            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  